Poster Abstract: A Scalable Coded Computing Framework for Edge-Facilitated Wireless Distributed Computing

2016 ◽  
Author(s):  
Songze Li ◽  
Qian Yu ◽  
Mohammad Ali Maddah-Ali ◽  
A. Salman Avestimehr
Keyword(s):  
Distributed Computing ◽  
Poster Abstract ◽  
Computing Framework

Research and Design of the Distributed Mass Small File Storage System Based on WCF

2013 ◽  
Vol 765-767 ◽  
pp. 1087-1091
Author(s):  
Hong Lin ◽  
Shou Gang Chen ◽  
Bao Hui Wang
Keyword(s):  
Fault Tolerance ◽  
Distributed Computing ◽  
Data Storage ◽  
Large Scale ◽  
Storage System ◽  
Hardware Platform ◽  
File Storage ◽  
Computing Framework ◽  
Research And Design ◽  
Small File

Recently, with the development of Internet and the coming of new application modes, data storage has some new characters and new requirements. In this paper, a Distributed Computing Framework Mass Small File storage System (For short:Dnet FS) based on Windows Communication Foundation in .Net platform is presented, which is lightweight, good-expansibility, running in cheap hardware platform, supporting Large-scale concurrent access, and having certain fault-tolerance. The framework of this system is analyzed and the performance of this system is tested and compared. All of these prove this system meet requirements.

Mobile Agent-Based Collaborative Computing Framework for Handling Constraint Resources

2012 ◽  
pp. 113-134
Author(s):  
Anil Kakarla ◽  
Sanjeev Agarwal ◽  
Sanjay Kumar Madria
Keyword(s):  
Information Processing ◽  
Distributed Computing ◽  
Mobile Agent ◽  
Computational Cost ◽  
Computation Time ◽  
Distributed Network ◽  
Collaborative Computing ◽  
Agent Based ◽  
Computing Framework ◽  
Viable System

Information processing and collaborative computing using agents over a distributed network of heterogeneous platforms are important for many defense and civil applications. In this chapter, a mobile agent based collaborative and distributed computing framework for network centric information processing is presented using a military application. In this environment, the challenge is to continue processing efficiently while satisfying multiple constraints like computational cost, communication bandwidth, and energy in a distributed network. The authors use mobile agent technology for distributed computing to speed up data processing using the available systems resources in the network. The proposed framework provides a mechanism to bridge the gap between computation resources and dispersed data sources under variable bandwidth constraints. For every computation task raised in the network, a viable system that has resources and data to compute the task is identified and sent to the viable system for completion. Experimental evaluation under the real platform is reported. It shows that in spite of an increase of the communication load in comparison with other solutions the proposed framework leads to a decrease of the computation time.

SAZD: A Low Computational Load Coded Distributed Computing Framework for IoT Systems

2020 ◽  
Vol 7 (4) ◽  
pp. 3640-3649
Author(s):  
Mingjun Dai ◽  
Ziying Zheng ◽  
Shengli Zhang ◽  
Hui Wang ◽  
Xiaohui Lin
Keyword(s):  
Distributed Computing ◽  
Computational Load ◽  
Computing Framework

GeoBeam: A distributed computing framework for spatial data

2019 ◽  
Vol 131 ◽  
pp. 15-22 ◽  
Author(s):  
Zhenwen He ◽  
Gang Liu ◽  
Xiaogang Ma ◽  
Qiyu Chen
Keyword(s):  
Distributed Computing ◽  
Spatial Data ◽  
Computing Framework

A Dynamic Service-Oriented Distributed Computing Framework for Evaluation of Computationally Expensive Black-Box Analyses

2014 ◽  
Author(s):  
George H. Cheng ◽  
Chao Qi ◽  
G. Gary Wang
Keyword(s):  
Distributed Computing ◽  
Service Oriented Architecture ◽  
Experimental Testing ◽  
Black Box ◽  
Small Scale ◽  
Distributed Computing Networks ◽  
Service Oriented ◽  
Computationally Expensive ◽  
Computing Framework ◽  
Load Balancing Algorithm

A practical, flexible, versatile, and heterogeneous distributed computing framework is presented that simplifies the creation of small-scale local distributed computing networks for the execution of computationally expensive black-box analyses. The framework is called the Dynamic Service-oriented Optimization Computing Framework (DSOCF), and is designed to parallelize black-box computation to speed up optimization runs. It is developed in Java and leverages the Apache River project, which is a dynamic Service-Oriented Architecture (SOA). A roulette-based real-time load balancing algorithm is implemented that supports multiple users and balances against task priorities, which is superior to the rigid pre-set wall clock limits commonly seen in grid computing. The framework accounts for constraints on resources and incorporates a credit-based system to ensure fair usage and access to computing resources. Experimental testing results are shown to demonstrate the effectiveness of the framework.

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